Coping With Unemployment: The Impact of Unemployment Duration and Personality on Trajectories of Life Satisfaction

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1 European Journal of Personality, Eur. J. Pers. 29: (2015) Published online 9 November 2015 in Wiley Online Library (wileyonlinelibrary.com).2034 Coping With Unemployment: The Impact of Unemployment Duration and Personality on Trajectories of Life Satisfaction ELISABETH HAHN 1 *, JULE SPECHT 2, JULIANA GOTTSCHLING 1 and FRANK M. SPINATH 1 1 Department of Psychology, Saarland University, Germany 2 Department of Psychology, Freie Universität Berlin, Germany Abstract: Unemployment is a major life event that causes an enormous drop in people s life satisfaction. However, there is substantial variability in people s ability (or inability) to cope with the experience of unemployment. In the present study, we examined the causes of individual differences in trajectories of life satisfaction when people were faced with unemployment by taking into account the persistence of unemployment, pre-event personality and age. Analyses were based on data from the German Socio-Economic Panel. Using latent growth curve modelling, life satisfaction was investigated from 3 years before to 3 years after a person became unemployed in a total sample of 908 individuals. As expected, unemployment caused a substantial drop in life satisfaction that persisted for at least 3 years after the event. On average, individuals did not completely return to their previous satisfaction level. This pattern existed even for participants who re-entered the labour market. Moreover, our results showed that variability in coping with unemployment can be explained in part by personality traits. For people with short periods of unemployment, Conscientiousness reinforced the negative effect of unemployment, whereas Extraversion softened the effect. In sum, our analyses showed that (a) the negative effect of unemployment on life satisfaction differs according to the length of the unemployment period and (b) personality partially moderates responses to unemployment over time. Copyright 2015 European Association of Personality Psychology Key words: life satisfaction; subjective well-being; life events; coping; personality traits; longitudinal modelling Studies on subjective well-being (SWB) have tried to shed light on questions such as How do life events affect people s well-being? by examining the causes and consequences of life satisfaction as well as positive and negative affect (Diener, Suh, Lucas, & Smith, 1999). An intuitive assumption is that important events have a long-lasting effect on a person s SWB. However, contradicting this assumption, studies have repeatedly demonstrated that, for instance, marriage, divorce or widowhood lead to only an initial rise or drop in SWB but that individuals return to their pre-event levels of SWB within a relatively short period of time (e.g. Campbell, Converse, & Rodgers, 1976; Diener, Lucas, & Scollon, 2009; Lykken & Tellegen, 1996). In sum, these results led to the set point theory of SWB (for reviews, see Diener, Lucas, & Scollon, 2006; Headey & Wearing, 1989), which states that an adult individual s level of SWB fluctuates around a stable set point and that even a dramatically negative or a very positive event exerts only relatively transient effects. But, critics question the generalizability of the set point theory, highlighting exceptions such as losing one s job, an event that can cause people to fail to return to their pre-event set point (Headey, 2010). With respect to unemployment, studies have consistently shown that the average level of SWB begins to decrease *Correspondence to: Elisabeth Hahn, Department of Psychology, Saarland University, Campus A1 3, D Saarbruecken, Germany. e.hahn@mx.uni-saarland.de Copyright 2015 European Association of Personality Psychology before the event even occurs and drops especially sharply in the year in which the unemployment occurs (Luhmann, Hofmann, Eid, & Lucas, 2012). However, with respect to the average rate of adaptation as well as the question of whether people ever fully return to their pre-event levels of SWB at all, results have been contradictory, and this raises the question of how different results can be explained. In addition to specific study characteristics (e.g. cultural context, political system or sample composition), the extent to which unemployment influences SWB can obviously vary according to various factors, such as the length of the period of unemployment, the age at which the person becomes unemployed or the anticipation of whether a person will be able to find a new job. Moreover, there is also substantial variation across individuals in their reactivity to job loss, a fact that is not yet well understood (Bonanno, 2004; Lucas, 2007; Yap, Anusic, & Lucas, 2012). Whereas some individuals suffer large decreases in SWB following unemployment, others show only small fluctuations in their well-being. It is likely that individual characteristics such as personality traits play an important role in moderating the extent to which people are affected by unemployment as well as their ability to find a new job quickly. In the present study, we investigated causes of individual differences in life satisfaction when people were faced with unemployment in a prospective design across a period of 7 years (from 3 years before to 3 years after the year in which the job was lost), using a large, longitudinal and nationally Received 28 February 2015 Revised 9 September 2015, Accepted 2 October 2015

2 636 E. Hahn et al. representative sample from the German Socio-Economic Panel (SOEP). We first examined trajectories of life satisfaction over time by taking into account the persistence of unemployment in terms of remaining unemployed versus finding a new job. We further tested a tenet of set point theory; that is, we tested whether individuals returned to their pre-event satisfaction levels within the 3 years following the event in subsamples of people who were unemployed for long-term versus short-term intervals. Finally, we investigated whether individuals with different personality trait levels reacted differently with respect to the trajectories of life satisfaction before and after the unemployment event in both groups. Sources of individual differences in life satisfaction Subjective well-being is widely understood as a broad construct encompassing general life satisfaction and domainspecific satisfaction as its cognitive components and positive and negative affect as its emotional components (Diener et al., 1999). The set point theory of SWB postulates that adults have relatively stable levels of SWB, with differences predominantly due to personality traits and other factors that are partly heritable or determined early in life. From the perspective of this theory, major life events can cause deviations from an individual s set point, but the effects of these events are usually transitory, and after a short period of time, people return to their previous set points. Studies linking the Big Five personality traits to SWB have established a consistent pattern of relations (DeNeve & Cooper, 1998). Previous studies have identified that SWB has a moderate negative relation with Neuroticism, moderate positive relations with Extraversion and Conscientiousness and a smaller positive relation with Agreeableness (DeNeve & Cooper, 1998; Specht, Egloff, & Schmukle, 2013; Steel, Schmidt, & Shultz, 2008). Overall, the most consistent relations have been confirmed for Extraversion and Neuroticism, whereby personality has been found to explain as much as 39% of the variance in quality-of-life measures (Steel et al., 2008). It has been hypothesized that Agreeableness and Conscientiousness may be prerequisites for creating more desirable life conditions (e.g. personal bonds), which in turn contribute to higher SWB (McCrae & Costa, 1991). Another stream of research has investigated the genetic and environmental sources of interindividual differences in SWB, finding not only substantial genetic influences (e.g. Bartels & Boomsma, 2009; Hahn, Johnson, & Spinath, 2013) but also that the long-term stability of SWB can be traced back to stable genetic influences (Lykken & Tellegen, 1996; Nes, Røysamb, Tambs, Harris, & Reichborn-Kjennerud, 2006), which might largely explain why SWB shows reasonable stability across the lifespan even after taking situational influences into account (Diener & Lucas, 1999). Life events (e.g. marriage, divorce, unemployment and birth of a child), situational factors (e.g. daily hassles and uplifts) and demographic factors (e.g. age, gender, income and educational background) have the potential to exert both short-term and long-term influences on SWB. Despite its moderate stability (Eid & Diener, 2004; Lucas & Donnellan, 2007; Schimmack, Krause, Wagner, & Schupp, 2010), SWB fluctuates as a reaction to major life events, but individuals tend to return to their pre-event levels of SWB (Diener et al., 2006; Lucas, 2007; Luhmann et al., 2012). Highly negative events, such as unemployment, widowhood or the onset of disability, seem to have a lasting negative effect on SWB (see Luhmann et al., 2012, for a review), but this effect has been found to be moderated by personality characteristics (e.g. perceived control; Specht, Egloff, & Schmukle, 2011a). Also, it has been stated that the effects of life events on SWB begin years before the event actually occurs, which in turn means that studies beginning shortly before or at the time of the relevant event may have missed pre-event declines in SWB (Clark, Diener, Georgellis, & Lucas, 2008). The case of unemployment It has been argued that negative events in particular can have long-lasting deleterious effects on SWB (Headey, 2010). This line of argumentation is especially apparent for the event of unemployment given that losing one s job can be devastating, humiliating and extremely harmful to the health and SWB of those affected (Frey & Stutzer, 2002). In the past 10 years, the unemployment rate in Germany has ranged from 5% to 10% (Statistisches Bundesamt). The consequences of unemployment include not only losses in earnings and social prestige, but also a loss of access to social relationships, lower self-esteem and a lower sense of selfworth. For example, according to a meta-analysis by McKee-Ryan, Song, Wanberg, and Kinicki (2005), unemployment showed an average causal effect size of 0.38 on mental health. Furthermore, job loss was shown to negatively influence motivation and self-respect (Sen, 1997), both important factors if one is trying to find a new job. Previous longitudinal studies were consistent in showing an average initial drop in SWB as a reaction to unemployment. However, depending on the time interval under study, individuals either slowly adapt or never returned to their initial SWB levels. Among other studies, Lucas, Clark, Georgellis, and Diener (2004) found that on average, individuals did not completely return to their former levels of satisfaction, even after they became reemployed (p. 8). Clark, Georgellis, and Sanfey (2001) also showed that recurring experiences of unemployment have such a scarring effect on individuals that most people do not fully recover. In a recently published meta-analysis, Luhmann et al. (2012) found a significant negative initial reaction followed by an increase in SWB (cognitive component), suggesting that people adapted to unemployment after approximately 3 years. Recent studies have focused on the extent of people s reactions and adaptations to major life events in relation to normative changes over time. Anusic, Yap, and Lucas (2014a) showed that intraindividual changes in SWB following the event of unemployment were significant but not compared with changes in SWB in a control sample of people who did not experience the event of unemployment. Although often not systematically considered, whether or not people find a new job after a period of unemployment could be a

3 Unemployment and Personality on Satisfaction 637 major determinant of people s life satisfaction. If individuals are unemployed for some time, they may become used to it in the sense of habituating to it, a process that could lead to a reduction in the initial drop in SWB and thus a renewed increase in SWB over time (Clark et al., 2001). By contrast, unemployment has been shown to inflict potential long-term damage with respect to social and health disadvantages (Wadsworth, Montgomery, & Bartley, 1999). Moreover, lower SWB seems to be not only a direct consequence of losing one s job, but it is also prospectively associated with the occurrence of unemployment in the sense that less satisfied people are more likely to lose their jobs (Luhmann, Lucas, Eid, & Diener, 2013). In addition, most studies have focused on only one specific life event within each sample and also controlled for only one particular life event in the respective control sample although other life events, which have their own negative or positive effects, may have co-occurred. For example, Kraft (2001) used data from the SOEP and concluded that a period of unemployment significantly increases the possibility of divorce, which in turn exerts a negative effect on SWB. Studies have also investigated the co-occurrence of unemployment and the experiences of marriage or parenthood and have found different effects for men and women. Schmitt (2008) found negative effects of unemployment on making a family for men, whereas unemployment increased women s likelihood of having their first child when the women had moderate or low levels of education. Given the initial positive effect of marriage and childbirth on SWB components (Luhmann et al., 2012), such events would buffer the true negative effect of unemployment on life satisfaction if not controlled for. Individual differences in reactions to unemployment Despite there being an average trajectory as a reaction to a major life event, research has shown that people react differently to the same circumstances (Lucas, 2007; Lucas, Clark, Georgellis, & Diener, 2003). Given that individuals evaluate life conditions and events according to their unique expectations and values, it seems plausible to expect that personality traits could explain different trajectories in SWB. Galatzer-Levy, Bonanno, and Mancini (2010), for example, investigated individual differences in SWB in response to unemployment using latent growth mixture modelling. They reported that the majority of individuals (82%) demonstrated stability over time with no long-term effects on SWB in response to unemployment, whereas 4% of individuals never fully returned to their pre-unemployment levels of SWB. On the basis of these results, the development of SWB before and after job loss cannot be reduced to a unified mean trend. The emerging question of why individuals differ in their reactions has yet to be answered. Recent studies have examined the moderating role of personality on change in SWB following the experience of certain life events. For instance, Yap et al. (2012) investigated the influence of personality on changes in SWB after unemployment using data from the British Household Panel Survey. The only significant effect that could be found was that people higher in Agreeableness initially reacted less negatively to unemployment. But, personality traits did not have a consistent moderating effect on the association between stressful life events and SWB changes, a result that has also been replicated in a Swiss national sample (Anusic, Yap, & Lucas, 2014b) as well as an Australian national sample (Anusic et al., 2014a). By contrast, Boyce, Wood, and Brown (2010) showed in the SOEP sample that individuals high in Conscientiousness experienced a significantly larger decrease in SWB than individuals low in Conscientiousness. In conclusion, unemployment can be a transitory situation that ends after some months or years for many people, but for others, unemployment persists and becomes a long-term condition. Nevertheless, despite the large body of research conducted on the event of unemployment, the results have been inconsistent, a fact that may be explained in part by the different methodological approaches applied in previous studies. Differences in the operationalization of the variables of interest (i.e. personality and SWB) may lead to different results across data sets when exploring relations between the same constructs. There may also be many subtle cultural differences among nations that impact the degree to which unemployment affects the life satisfaction of individuals within a population, even among highly similar western nations. How a society deals with unemployment, values work and unemployment and supports the affected people financially and socially could influence the effect of unemployment on SWB. The selection criteria typically applied in previous studies has included individuals who indicated that they were not unemployed when the study began but who experienced at least one bout of unemployment during the course of the study. However, by defining the sample in this way, that is, by considering only the event itself without noting the duration of the event over time, the persistence of unemployment could not be systematically examined. Furthermore, past research has not consequently controlled for the co-occurrence of other major life events that may have had an additional positive or negative effect on the level of SWB. In addition, only a few studies (e.g. Anusic et al., 2014a; Yap et al., 2012) have addressed variability in SWB as a reaction to unemployment, but still, most of the individual differences in SWB before and after the event of unemployment remain unexplained. More precisely, whereas these studies replicated the associations between personality and the baseline level of SWB prior to the event, personality did not explain change in SWB after the experience of unemployment. Finally, when studying unemployment, age may also have an influence on the strength of the effect. Unemployment might be more or less adverse depending on a person s age and the person s associated likelihood of finding a new job. For example, older individuals who wish to find employment may face real or perceived job discrimination. Initial evidence for the impact of age comes from Luhmann et al. (2012), who reported negative associations between age and the effect of unemployment on SWB, but only among younger age groups. AIMS OF THE PRESENT STUDY The present study had three aims. First, we examined whether the pre-event level of life satisfaction, the initial reaction to the event and long-term changes in life satisfaction

4 638 E. Hahn et al. were comparable among a group of people who were unemployed for a long-term interval (i.e. people who remained unemployed across the 3 years following the event) and a group of individuals who were unemployed for a short-term interval (i.e. people who found a job again within the 3 years of assessment after the event). Second, we examined the combined effect of all Big Five personality traits on trajectories of life satisfaction as a reaction to unemployment. In contrast to former work, we included all of the traits in one model to control for shared variance between the personality traits, thus providing a more complete picture of the influence that prior-event personality exerts on changes in life satisfaction. Third, we included age as a further explanatory variable in our analyses. We also controlled for other life events (i.e. marriage, divorce, childbirth and death of a spouse) that might have either a buffering or an aggravating effect on the trajectory of SWB. This is particularly important as former research has shown that life events do not happen randomly (e.g. Specht, Egloff, & Schmukle, 2011b). To the best of our knowledge, this is the first study to examine the influence of personality on the course of life satisfaction as a reaction to unemployment for the German population using data from the German SOEP while taking these influences into account. METHOD For the purpose of the present analyses, we used data from the SOEP (Wagner, Frick, & Schupp, 2007), a large, ongoing, nationally representative survey of approximately private households in Germany. The main SOEP topics include health, childcare and educational history, employment history and current employment status, income, living situation, SWB and certain attitudes (Wagner et al., 2007). The response rates for the survey were generally very high (ranging from 60% to 70% across various subsamples; see Haisken-DeNew & Frick, 2005, for details). Annual data collection began in 1984, whereby the latest wave of data included in the present analyses was collected in 2012 comprising a total of adults. The use of the SOEP comes along with many benefits, including the opportunity to use prospective data on life satisfaction and personality, annual information on changes in life conditions (i.e. life events), sufficiently large samples to examine even rare life events and generalizability to the German population. Sample selection of groups who experienced the event of unemployment Previous research has suggested that several specifications have to be made with respect to the time interval under consideration and the sample selection criteria. For the selection of the present sample, we followed the procedures of previous studies to a large extent but also made a few different decisions. We selected individuals who experienced the event of unemployment after the first time we measured their personality in By doing so, we ensured that potential moderating effects of prior personality on future unemployment and changes in life satisfaction after the event could be interpreted as independent of possible subsequent changes in personality traits caused by the event (e.g. Boyce, Wood, Daly, & Sedikides, 2015; Specht et al., 2011b). For each person, we examined a time interval of 7 years: 3 years prior to the event, the year in which the event occurred and 3 years after the event. We first identified people who became unemployed in the years 2006, 2007, 2008, 2009 or To establish a stable baseline for life satisfaction prior to the event, we subsequently selected individuals from this sample of unemployed persons who had been employed for the three consecutive waves before the event of unemployment. Thus, the years prior to the event set the baseline for life satisfaction. Given that we were interested in the effect of unemployment among the typical working population, we limited the sample to individuals who had become unemployed before the age of 60 years to avoid a confounding of unemployment with retirement during the time period considered (for a similar approach, see Galatzer-Levy et al., 2010). In the next step, we controlled for the experience of additional major life events (i.e. marriage, divorce, childbirth and death of a spouse). Finally, we included only individuals with valid information on the personality scales in 2005 (at least two of the three items per scale) and restricted the sample to individuals who provided life satisfaction data in at least one wave. The percentage of missing values in life satisfaction ranged between 1% and 30% per year. The final sample of 908 individuals consisted of 128 persons who became unemployed and remained unemployed for the subsequent 3 years of assessment (group of long-term unemployed). The remaining 780 individuals showed mixed patterns of unemployment and re-employment (group of short-term unemployed; see Table 1). Within this mixed group, 251 individuals showed only a short period of unemployment of 1 year in the event year, 208 individuals were unemployed for 2 years and were employed again, 49 individuals were reemployed after 3 years and the remaining 272 individuals showed multiple periods of unemployment with 1 or 2 years of employment in between. Measures Big Five In the SOEP, personality is measured with the SOEP Big Five Inventory (BFI-S) (Gerlitz & Schupp, 2005), a 15-item Table 1. Demographic characteristics of the total sample and the two subsamples Age in the year of Unemployment Sample n M SD % Women % Middle vocational Total Short-term unemployed Long-term unemployed

5 Unemployment and Personality on Satisfaction 639 version of the Big Five inventory (John, Donahue, & Kentle, 1991). The BFI-S assesses the Big Five personality traits by means of three items per dimension. Participants rated these statements on a 7-point Likert scale ranging from 1 (does not apply to me at all) to7(applies to me perfectly). Item selection and construction of the BFI-S is described in detail by Gerlitz and Schupp (2005). The BFI-S scales in general have shown acceptable levels of internal consistency, stability over time and convergent validity in relation to the NEO Personality Inventory Revised (NEO-PI-R) dimensions (Hahn, Gottschling, & Spinath, 2012). In the present sample, Cronbach s α for the five scales ranged from.51 (Agreeableness) to.70 (Extraversion) with a mean α of.62. To account for measurement error, the personality factors were included as latent variables in the model. In line with the NEO-PI-R manual (Ostendorf & Angleitner, 2004), we found BFI-S scale intercorrelations that ranged from.05 (Openness and Neuroticism, ns) to.36 (Extraversion and Openness, p <.01). Life satisfaction Every year, respondents were asked to provide self-reported answers to the question How satisfied are you with your life, all things considered? Participants responded with ratings on an 11-point scale ranging from 0 (completely dissatisfied) to 10 (completely satisfied). Previous studies have shown that the performance of single-item life satisfaction measures is very similar to a more psychometrically established multiple-item scale, the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). By using multivariate latent state-trait models, Lucas and Donnellan (2007) showed that the reliability of this measure in the SOEP is at least.67 (calculated as 1 minus the state/error variance component). Mean life satisfaction scores for the complete SOEP sample ranged from 6.80 to 7.11 in the years 2003 to Standard deviations ranged from 1.73 (2012) to 1.83 (2004). To control for year-specific life satisfaction trends, we centred the satisfaction scores within each year using the average value of the total SOEP sample. Thus, a score of zero on the life satisfaction measure reflects the average level of satisfaction for a particular year, whereas individual values greater than zero indicate greater life satisfaction compared with the sample as a whole. Statistical model Previous research has suggested that nonlinear trajectories can be expected for life satisfaction, especially in the years surrounding a major life event, meaning that life satisfaction seems to change more quickly in the years surrounding the event, but the rate of development slows later on. Therefore, we specified a nonlinear latent growth curve model (LGCM; Willett & Sayer, 1994; Figure 1) that could be used to capture more complex trajectories of change compared with a simple linear LGCM. LGCMs are designed to measure both intraindividual and interindividual variability in change. Repeated measurements of a given variable are modelled as a function of two latent factors (i.e. intercept and slope). We assessed life satisfaction 3 years before the event, during the event year itself and 3 years after the event. The intercept reflects the average starting point for life satisfaction, and the slope was used to model the main course of life satisfaction prospective to and in reaction to the event. Variability in the intercept and slope indicates differences among individuals in terms of individual starting points in life satisfaction and the extent of change, respectively. In order to model the nonlinearity of the growth process, only two loadings on the slope were fixed: the loading of life satisfaction for the first measurement point was fixed to 0 in order to establish a baseline level, and the loading in the year of unemployment was fixed to a negative value ( 1, 1.5) 1 to indicate the initial drop in life satisfaction as a reaction to becoming unemployed. In the full model, all other loadings on the slope were freely estimated (Meredith & Tisak, 1990) because growth may be positive or negative. These free loadings increased the flexibility of the model in fitting the nonlinear forms of change that best fit the data. Similar to a linear LGCM, all of the loadings were fixed to 1 on the intercept factor. We further tested the set point model by comparing the model described in the previous text with another model in which the loading of life satisfaction on the slope at the last measurement point (3 years after the event) was fixed to 0. This model comparison tested whether individuals returned to their baseline levels of life satisfaction within 3 years after the event independent of the initial decline in life satisfaction. To investigate the effect of unemployment duration, these two models were applied to the total sample as well as to the subsample of individuals who showed long-term unemployment and the subsample of individuals who became re-employed again within the 3 years of assessment after the event. To investigate whether personality and age could predict patterns of change in life satisfaction prospective to and in reaction to unemployment, we extended the basic model (Figure 1) by including all Big Five personality dimensions and age as explanatory factors in a second set of analyses. In these models, the trajectory parameters (i.e. intercept and slope) were treated as outcomes that could be directly influenced by the explanatory factors. Each effect of an explanatory factor on the intercept and slope could be interpreted the same way as in the traditional regression model, thus reflecting how differences in personality or age were associated with a corresponding shift in intercept and slope. To evaluate the true effect of each personality factor in the presence of the other personality characteristics, we modelled all five personality factors as well as their intercorrelations simultaneously. This was also performed to avoid overestimating the relevance of a single personality factor when modelled separately. All models were estimated with Mplus Version 6 (Muthén & Muthén, 1998) using the full information maximum 1 First, we ran the analyses using the total sample as well as the subsample with a fixed negative value of 1 in the year of unemployment for all three groups. However, the overall model fit for the group that was unemployed for the long-term was not satisfactory, and an inspection of life satisfaction means for the 7 years of assessment in the three groups indicated that the mean in life satisfaction for the group that was unemployed for the long-term was significantly lower in the year of unemployment compared with the means of the short-term group and the total sample. To improve the overall model fit and to set a more appropriate decline for the long-term group, we fixed the loading of life satisfaction to a negative value of 1.5 in the year of the event of unemployment for the long-term group.

6 640 E. Hahn et al. Figure 1. Latent growth curve model (LGCM). The top path estimates refer to the total sample, the middle values refer to the estimates derived in the short-term group and the bottom values refer to estimates derived in the long-term sample. All intercept paths are fixed to 1. Slope paths are fixed to 0 for baseline life satisfaction (ls-3) and to 1 and 1.5, respectively, for the time of the event (ls 0). ls, life satisfaction. p <.05. likelihood method, which accounts for missing data. The evaluation of model fit was based on multiple criteria. The overall model fit was evaluated with the chi-square statistic (χ 2 )in combination with the root mean square error of approximation, the standardized root mean square residual and the comparative fit index. As shown by simulation studies, a comparative fit index greater than.95, a root mean square error of approximation smaller than.06 and a standardized root mean square residual smaller than.08 are indicators of good model fit (Hu & Bentler, 1999). Nested models were compared with the full model using the likelihood ratio test (χ 2 -difference test), with a nonsignificant p-value (p >.05) indicating that the simpler model could be accepted without a significant loss of fit. We used this in combination with the Akaike information criterion, with lower values indicating a better fit. RESULTS In our presentation of the results, we describe all models with respect to the total sample as well as the subsamples of individuals who experienced short-term or long-term intervals of unemployment. Results for the total sample can be used to answer more general questions about whether unemployment is associated with lasting change in people s satisfaction levels in the German population and additionally provide a basis for comparison with previous research on life events that used a similar methodology in different nations. The question of whether and how persistent unemployment influences changes in life satisfaction can be answered by referring to the two subsamples of people who were unemployed for short-term and long-term intervals. We first describe the results of the LGCM without any explanatory factors followed by the conditional models that included personality and age as predictors of the trajectories of life satisfaction. Latent growth curve model without explanatory factors The model fit statistics for all LGCMs are presented in Table 2. For all basic LGCMs (total sample, short-term subsample and long-term subsample), the unconstrained model fit significantly better than the alternative set point model with the additional constraint of a 0 loading of life satisfaction on the slope 3 years after the occurrence of unemployment (ls 3). In contrast to the assumptions put forward by the set point theory, this finding indicates that the average satisfaction level at the end of our observation period remained lower than the initial baseline level. Thus, people reported lower life satisfaction 3 years after the experience of unemployment. Figure 2 illustrates the modelled trajectories of life satisfaction separately for the three groups on the basis of estimates of path coefficients and latent growth parameters derived from the LGCM. 2 For individuals who experienced long-term unemployment, the average level of life satisfaction at the beginning was lower, and the decline in response to unemployment was stronger. In addition, 3 years after the event, their satisfaction levels were also lower compared with the predicted course for individuals who experienced short-term unemployment. We found a negative mean score for the intercept of 0.58 (p <.001) only for the long-term group, indicating that those individuals had begun the study with a significantly lower satisfaction level than the average SOEP sample. This effect did not appear in either the total sample or the short-term subsample. However, the results also showed significant variation between people in each of the groups (s I total = 1.76; s I short-term = 1.73; s I long-term = 1.92; p <.001). With respect to the slope parameter, all models indicated significant change but also individual differences in the strength of the reaction for the total sample as well as for the short-term subsample. This effect did not appear in the long-term sample, which means that the trajectories for changes in life satisfaction were more homogeneous in this group. Finally, the models for the total sample and the short-term group implied a positive correlation between the intercept and slope factors, which means that those individuals with higher values at the beginning tended to have higher slope scores or a greater change (i.e. a steeper decline) in life satisfaction over time. 2 Illustrated courses of life satisfaction for the three groups can be easily reproduced by using the following formula: LS t = M I +(b S )*M S, where LS reflects the level of life satisfaction and t indicates the point in time relative to the experience of unemployment. The parameter b S is the growth parameter of the slope (S), M S is the mean of the slope and M I indicates the mean of the intercept (I).

7 Unemployment and Personality on Satisfaction 641 Table 2. Summary of model fit statistics for the LGCMs (basic) and the conditional LGCMs including personality and age Model χ 2 (df) p CFI RMSEA SRMR AIC Basic Total (18) Total set point (19) Short-term (18) Short-term set point (19) Long-term (18) Long-term set point (19) Including personality and age Total (207) Total set point (208) Short-term (207) Short-term set point (208) Long-term (206) Long-term set point (207) Note: LGCM, latent growth curve model; df, degrees of freedom; CFI, comparative fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual; AIC, Akaike information criterion. Set point models = ls 3 fixed to 0; best-fitting models marked in bold. Figure 2. Different life satisfaction trajectories based on the best-fitting model for each group. Conditional latent growth curve model We expanded the basic LGCM (Figure 2) by adding explanatory factors to the model consisting of the Big Five personality traits as latent factors and age as a manifest variable. The results of the tests of model fit for the conditional LGCM are summarized in Table 2. Estimates of the intercept and slope parameters and path coefficients for the effects of personality and age on the latent growth parameters are presented in Table 3. The overall model fit for the conditional models was not as good as for the basic LGCM, a difference that could be due to the complexity of the models in relation to sample size. For example, fit indices may indicate that a model has good fit when, in fact, parts of the model may fit poorly or vice versa (Tomarken & Waller, 2003). Focusing on the effect of personality, the results supported a general association between personality and the baseline level of life satisfaction (i.e. the intercept). In the present sample, we found that Neuroticism showed a strong negative relation to life satisfaction independent of the persistence of unemployment (b total =.28, b short-term =.26, b long-term =.34, p <.001). In thesamemanner,agealsosignificantly influenced the starting point for life satisfaction, indicating that older individuals already had lower satisfaction levels 3 years before the experience of unemployment, independent of the actual duration of the unemployment period. Furthermore, the analyses revealed a positive effect of Agreeableness on the intercept of life satisfaction (b total =.19, b short-term =.18, p <.001) but only for the total sample and the subsample of people who were unemployed for the short-term interval. With respect to the slope, the models revealed significant associations between personality and the life satisfaction trajectories but only in the more heterogeneous groups of individuals with short-term experiences of unemployment and the total sample. This is not surprising as there was no significant individual variation in the long-term group. Thus, personality traits moderated changes in satisfaction levels following the experience of unemployment only if the period of unemployment was less than 3 years. Most important, Extraversion predicted a smaller decrease in satisfaction levels in reaction to unemployment (b total =.18, b short-term =.21, p <.05). In addition, Conscientiousness was positively related to changes in satisfaction levels, but the effect was significant only in the total sample (b total =.18, p <.05, b short-term =.17, p <.10). With respect to age, the models revealed no significant associations between change in life satisfaction and age in the year of the event. In sum, the drop in life satisfaction was less pronounced for individuals scoring higher on Extraversion and lower on Conscientiousness. For the subsample of people who remained unemployed for at least the 3 years of assessment in our study, personality did not moderate short-term or long-term changes in life satisfaction. Altogether, personality and age explained 18% to 30% of the variance in life satisfaction baseline levels and 4% to 16% of the variance in the life satisfaction slope. A substantial proportion of variance in both the intercept and slope in the total sample and in the short-term sample remained unexplained independent of the influence of personality and age. DISCUSSION The aim of the present study was to examine the degree to which the Big Five personality traits and age could account

8 642 E. Hahn et al. Table 3. Estimations for intercept and slope, effects of personality and age based on conditional nonlinear LGCM in the three samples Model x s ris N E O A C Age R * 0.23* [ 0.32, 0.15] 0.05 [ 0.07, 0.17] 0.19* [0.07, 0.30] 0.04 [ 0.09, 0.17] 0.01 [ 0.13, 0.10] 0.28* [ 0.37, 0.18] 0.27* [0.11, 0.42] 1.39* [1.19, 1.58] Total I 0.85* [0.51, 1.20] [ 0.19, 0.05] 0.18* [0.01, 0.35] 0.05 [ 0.21, 0.12] 0.13 [ 0.06, 0.31] 0.18* [ 0.36, 0.01] 0.07 [ 0.21, 0.07] 0.51* [0.28, 0.73] S 0.50* [0.21, 0.78] 0.18* 0.20* [ 0.30, 0.11] 0.04 [ 0.10, 0.18] 0.18* [0.05, 0.31] 0.06 [ 0.09, 0.21] 0.02 [ 0.15, 0.12] 0.26* [ 0.36, 0.15] 0.36* [0.15, 0.57] 1.41* [1.17, 1.64] Short-term I 0.83* [0.44, 1.23] [ 0.21, 0.08] 0.17 [ 0.02, 0.37] 0.10 [ 0.28, 0.09] 0.21 [ 0.00, 0.42] 0.21* [ 0.41, 0.01] 0.05 [ 0.21, 0.11] 0.65* [0.35, 0.95] S 0.57* [0.20, 0.95] 0.30* 0.25* [ 0.46, 0.04] 0.07 [ 0.61, 0.47] 0.24 [ 0.45, 0.93] 0.12 [ 0.44, 0.68] 0.06 [ 0.64, 0.52] 0.34* [ 0.62, 0.06] 0.05 [ 0.09, 0.18] 1.34* [0.91, 1.77] Long-term I 0.62 [ 0.44, 1.68] [ 0.35, 0.23] 0.09 [ 0.67, 0.85] 0.45 [ 0.55, 1.46] 0.00 [ 0.79, 0.79] 0.26 [ 1.07, 0.56] 0.05 [ 0.35, 0.44] 0.11 [ 0.02, 0.23] S 0.30 [ 0.09, 0.70] Note: LGCM, latent growth curve model; x, mean; s = residual variance; ris = correlation coefficient of intercept and slope; N, Neuroticism; E, Extraversion; O, Openness to experience; A, Agreeableness; C, Conscientiousness; R 2, coefficient of determination; I, intercept; S, slope. For ease of interpretation, estimates for personality and age on I and S are standardized. The remaining estimates refer to unstandardized values. 95% Confidence interval in brackets.*p <.05. for the variability in life satisfaction changes prior and in response to the experience of unemployment depending on whether individuals remained unemployed or found a job again within the first 3 years after unemployment. First, the results of our study supported the results of past studies (Clark et al., 2008; Lucas et al., 2004; Yap et al., 2012) by reinforcing the view that the experience of unemployment is a stressful life event that causes a substantial drop in life satisfaction that persists for many years following the event. Most important, our results clearly showed for the first time that personality measured prior to the event of unemployment influences individuals psychological reactions to short-term and long-term unemployment and their abilities to cope with the experience or find a job again. Overall, soon-to-be unemployed individuals already indicated lower satisfaction levels in the year before they actually lost their jobs, and these levels then further declined as a reaction to the event of unemployment and did not recover within the 3 years following this stressful event. This pattern even existed in the subsample of participants who re-entered the labour market within the 3 years following unemployment. Using a different SOEP sample, Lucas et al. (2004) also reported that, even when individuals eventually regained employment, declines in life satisfaction occurred for many years after the unemployment event. In the same manner, Clark et al. (2001) found that people tended to be less satisfied if they had been unemployed at some point in the past. However, although adaptation to the baseline level was not complete in general within the 3 years following the event, the amount of adaptation was greater for the re-employed group as compared with the nearly complete lack of adaptation in the group of individuals who were unemployed for the longer period of time. This suggests that the persistence of unemployment plays an important role in this process and hence should be considered in future studies. Nonetheless, in the present sample, our investigation lasted for only 3 years after the event, and we therefore could not test whether and to what extent individuals recovered in subsequent years. Furthermore, the results largely correspond to previous findings that personality traits show robust associations with the baseline level of life satisfaction (Anusic et al., 2014a, 2014b; Yap et al., 2012). The strength of the effects between personality and life satisfaction were generally consistent with the existing literature (Hahn et al., 2013; Steel et al., 2008). As expected, Neuroticism was most consistently associated with lower satisfaction levels, whereas Agreeableness was associated with higher baseline satisfaction levels. At this point, our results tie in with findings in the field of behaviour genetics. A number of studies have reported that genetic influences account for as much as 30% to 50% of the phenotypic variance in SWB measures (e.g. Bartels & Boomsma, 2009; Hahn et al., 2013), supporting the idea of a genetically influenced life satisfaction set point. Furthermore, Hahn et al. (2013) showed that genetic influences on life satisfaction were completely shared with personality, mostly Neuroticism. People with elevated levels of Neuroticism may already perceive life as more stressful and may be more dissatisfied with their social support, aspects that could account for lower

9 Unemployment and Personality on Satisfaction 643 satisfaction levels (McCrae, 1990) especially in relation to the event of unemployment. In contrast to the negative influence of Neuroticism, it appears that Agreeableness exerts a protective function that may also be associated with the ability to find a new job. For example, Agreeableness can predict the quality of social relationships (Berry, Willingham, & Thayer, 2000), and such relationships in turn positively influence mental health and health-related behaviours (Umberson & Montez, 2010). Promoting mental health is extremely important for unemployed individuals given that mental illness actually has a significant negative effect on re-employment opportunities (Paul & Moser, 2009). Furthermore, our results demonstrate that variability in coping with the experience of unemployment can be explained in part by individual differences in the Big Five personality traits in the present nationally representative German sample. Without controlling for the persistence of unemployment, Extraversion as well as Conscientiousness appeared to influence changes in life satisfaction. Although these effects were relatively small, highly extraverted individuals experienced a considerably smaller decline in life satisfaction following the event of unemployment than did introverted individuals, although Extraversion did not influence individuals starting points in satisfaction. Similarly, a study by Pai and Carr (2010) showed that Extraversion buffered individuals against depressive symptoms in reaction to late-life spousal loss. For Conscientiousness, the results suggested the opposite effect, with greater negative reactions for highly conscientious individuals as compared with less conscientious individuals. Again, with respect to the effect size, the influence of Conscientiousness seemed to be rather small. However, these results were in line with findings reported by Boyce et al. (2010), who showed that although conscientious individuals generally tended to achieve more and had higher satisfaction levels, this positive relation was reversed after unemployment. As a possible explanation, one might assume that the value of work and the failure represented by unemployment were more important to more conscientious people, who therefore suffered more from the experience than less conscientious people (Boyce et al., 2010). With regard to the persistence of unemployment, these effects were present only in the subsample of re-employed individuals, supporting the idea that certain personality characteristics not only moderated the reaction to the event itself but also influenced people s ability to find a new job. In this context, Burger and Caldwell (2000) demonstrated in a sample of college students that high Extraversion predicted a greater use of social sources when seeking employment information and greater success in obtaining a job interview. However, previous studies have also indicated significant influences of the remaining personality traits, especially Conscientiousness. These studies did not control for shared variance components between the Big Five, although correlations between the Big Five factors exist and can reach substantial levels (e.g. Ostendorf & Angleitner, 2004). In the present sample, the relations of Extraversion and Conscientiousness to general levels of life satisfaction became less important after controlling for the influences that were shared between Extraversion and Neuroticism. Moreover, our results revealed that age had a negative effect on the baseline level of life satisfaction but did not moderate changes in life satisfaction in reaction to unemployment, with both results being independent of the duration of unemployment. With cross-sectional data, Blanchflower and Oswald (2008) tested the association between age and life satisfaction and found that life satisfaction seemed to decrease from young adulthood to middle age, reaching a minimum at around the age of 47 years. However, average levels of life satisfaction in the SOEP sample have been shown to be rather consistent from the late teens to the early 70s before declining quite steeply thereafter (almost three-quarters of a standard deviation) until the end of life (Baird, Lucas, & Donnellan, 2010; Gerstorf et al., 2010). Altogether, the results of our study clearly reveal that individual differences in changes in life satisfaction after the event of unemployment can be explained to a certain degree by personality traits but that the majority of variance in the slope factor of short-term unemployed still remains unexplained. This raises the question of which additional individual characteristics or capabilities can explain the remaining variance in satisfaction set points and changes. Apart from the Big Five personality factors, the effect of coping strategies, social support or self-esteem (DeNeve & Cooper, 1998) could have an impact on life satisfaction trajectories. Moreover, Wilson and Gilbert (2008) proposed that people adapt to their pre-event levels of SWB as soon as they find an explanation for the event. For unemployment, an important aspect that has so far been neglected may involve whether individuals actively quit or were fired. Luhmann et al. (2012) categorized life events as more or less likely to be actively influenced by the person himself or herself and found that the initial reaction to events in the former category was weaker than the initial reaction to events in the latter category, but there were no systematic differences in the rate of adaptation. In the present sample, the proportion of those who actively initiated unemployment (e.g. quit their jobs) was less than 10% and therefore could not be systematically investigated in the present study. With respect to Luhmann et al. (2012), our results are more of a reflection of the history of those who experienced unemployment as an event that could not be controlled, although it is unclear whether the event could have been anticipated or not. Further investigations including information about the circumstances of unemployment are needed in order to better understand the phenomenon and to further explain individual differences in the reaction to short-term unemployment in particular. It is important to note that the reaction to long-term unemployment seems to be very homogeneous, leaving only a little room for individual differences in reactions to this event. Although we were able to overcome some shortcomings of previous studies, one limitation of the current study is that it involved only a cognitive measure of SWB, that is, general life satisfaction. Previous studies have reported associations among different SWB dimensions in the range of.50 to.60 (e.g. Bartels & Boomsma, 2009), so it is possible that we would have obtained different results if we had used affective measures of SWB (Lucas, Diener, & Suh, 1996). Luhmann et al. (2012), for example, found divergent effects on